Experimental investigation on use of regularization techniques and pre-post measurement changes for structural damage identification

Practical damage identification often suffers from the ill-posedness and the unavoidable model errors. On the one hand, the regularization techniques including the Tikhonov regularization and the sparse regularization have been well recognized as effective tools to circumvent the ill-posedness. On the other hand, the model errors render the established baseline/analytical model inconsistent with the real structural and thereof, would introduce unexpected errors into damage identification. To deal with the model errors, the two-step strategy is often adopted for damage identification in practice: firstly update the baseline model so as to minimize the inconsistence and secondly, identify the damage upon the updated baseline model; Another empirical and not-yet-widely-recognized way is to use the measurement changes and motivated by this, a new MC (Measurement Changes) strategy that simply revises the measured data with measurement changes is developed in this paper for sensitivity-based damage identification. This work aims to present experimental investigations and comparative studies on the possible remedies for the difficulties in damage identification: the Tikhonov or sparse regularization for the ill-posedness and the two-step or MC strategy for the model errors. Final results show that the MC strategy is rather effective and efficient for damage identification and synthesis of the sparse regularization and the MC strategy is found as the appreciated way to practical damage identification.